OpenRaiser/Intern-Atlas huggingface dataset:
Cross-Learning from Scarce Data via Multi-Task Constrained Optimization
Venue: arXiv cs.LG 2025
Year: 2025

A learning task, understood as the problem of fitting a parametric model from supervised data, fundamentally requires the dataset to be large enough to be representative of the underlying distribution of the source. When data is limited, the learned models fail generalize to cases not seen during training. This paper introduces a multi-task \emph{cross-learning} framework to overcome data scarcity by jointly estimating \emph{deterministic} parameters across multiple, related tasks. We formulate this joint estimation as a constrained optimization problem, where the constraints dictate the resulting similarity between the parameters of the different models, allowing the estimated parameters to differ across tasks while still combining information from multiple data sources. This framework enables knowledge transfer from tasks with abundant data to those with scarce data, leading to more accurate and reliable parameter estimates, providing a solution for scenarios where parameter inference from limited data is critical. We provide theoretical guarantees in a controlled framework with Gaussian data, and show the efficiency of our cross-learning method in applications with real data including image classification and propagation of infectious diseases.
Efficient Calibration for Decision Making
Venue: arXiv cs.LG 2025
Year: 2025

A decision-theoretic characterization of perfect calibration is that an agent seeking to minimize a proper loss in expectation cannot improve their outcome by post-processing a perfectly calibrated predictor. Hu and Wu (FOCS'24) use this to define an approximate calibration measure called calibration decision loss ($\mathsf{CDL}$), which measures the maximal improvement achievable by any post-processing over any proper loss. Unfortunately, $\mathsf{CDL}$ turns out to be intractable to even weakly approximate in the offline setting, given black-box access to the predictions and labels. We suggest circumventing this by restricting attention to structured families of post-processing functions $K$. We define the calibration decision loss relative to $K$, denoted $\mathsf{CDL}_K$ where we consider all proper losses but restrict post-processings to a structured family $K$. We develop a comprehensive theory of when $\mathsf{CDL}_K$ is information-theoretically and computationally tractable, and use it to prove both upper and lower bounds for natural classes $K$. In addition to introducing new definitions and algorithmic techniques to the theory of calibration for decision making, our results give rigorous guarantees for some widely used recalibration procedures in machine learning.
Free-Form Scene Editor: Enabling Multi-Round Object Manipulation like in a 3D Engine
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in text-to-image (T2I) diffusion models have significantly improved semantic image editing, yet most methods fall short in performing 3D-aware object manipulation. In this work, we present FFSE, a 3D-aware autoregressive framework designed to enable intuitive, physically-consistent object editing directly on real-world images. Unlike previous approaches that either operate in image space or require slow and error-prone 3D reconstruction, FFSE models editing as a sequence of learned 3D transformations, allowing users to perform arbitrary manipulations, such as translation, scaling, and rotation, while preserving realistic background effects (e.g., shadows, reflections) and maintaining global scene consistency across multiple editing rounds. To support learning of multi-round 3D-aware object manipulation, we introduce 3DObjectEditor, a hybrid dataset constructed from simulated editing sequences across diverse objects and scenes, enabling effective training under multi-round and dynamic conditions. Extensive experiments show that the proposed FFSE significantly outperforms existing methods in both single-round and multi-round 3D-aware editing scenarios.
Boosting performance: Gradient Clock Synchronisation with two-way measured links
Venue: arXiv cs.DC 2025
Year: 2025

This master thesis extends the formal model of the GCS algorithm as presented by (Fan and Lynch 2004, 325), (Lenzen, Locher and Wattenhofer 2008, 510) and (Függer et al. 2023) to operate under implementation-near assumptions by replacing the one-way measurement paradigm assumed in prior work by the two-way measurement paradigm. With this change of paradigm, we remove many restrictions previously enforced to allow provable performance. Most notability, while maintaining the core behaviour of GCS, we: 1. Lift the requirement for unitary link lengths and thereby create a realistic model for flexible deployment of implementations of GCS in practice. 2. Provide a formal model of frequency sources assumed in prior work. 3. Perform a fine grained distinction between the different components of the algorithm's estimation error and globally reduce its impact by multiple orders of magnitude. 4. Significantly reduce the contribution of the uncertainty to the algorithm's estimation error to be in the range of 10\% to 0,1\% of the delay per link instead of being in the oder of the delay per link as in prior work and show matching upper bounds on the local and global skew of GCS.
US Code growth 1991-2025
Venue: arXiv cs.DL 2025
Year: 2025

This is the first scientific article since 2010 counting the words which are effective and permanent federal law in the United States (US) Code. The latest version of the US Code --published in 2025-- is the largest since 1991, encompassing over 24.4 million words. The low since 1991 was 1993 at roughly 15 million words. The word count grew in 30 out of 33 years. The average characters per word --potentially indicative of complexity-- grew even in all 33 years. The research also touches upon why it is undesirable for laws to be longer and longer and mentions possible remedies.
The current state of open access
Venue: arXiv cs.DL 2025
Year: 2025

This article presents reflections from the perspective of a university librarian involved in the establishment and management of institutional repositories in Japan. It examines the historical evolution of scholarly communication, from the oral exchanges of ancient Greek philosophers, through the advent of printing and the rise of academic journals, to the contemporary digital era. The origins of the open access movement are emphasized as rooted in authors' desire to disseminate knowledge globally, rather than merely opening access for readers. The article critically discusses current practices in Japan, including institutional repositories, open access journals, and "read-and-publish" agreements, highlighting that many digital innovations still imitate the conventions of print-based scholarly communication. Furthermore, it explores the challenges and opportunities posed by electronic information dissemination, including the limitations of the Version of Record and the potential of diamond open access models. The article argues that genuine progress in scholarly communication requires rethinking publication practices, embracing the modifiability of digital content, and developing new communication models fully suited to the internet era. Ultimately, the goal is to realize a global, real-time, trustworthy scholarly communication system that transcends the historical constraints of print media.
Multi-Agent VLMs Guided Self-Training with PNU Loss for Low-Resource Offensive Content Detection
Venue: arXiv cs.LG 2025
Year: 2025

Accurate detection of offensive content on social media demands high-quality labeled data; however, such data is often scarce due to the low prevalence of offensive instances and the high cost of manual annotation. To address this low-resource challenge, we propose a self-training framework that leverages abundant unlabeled data through collaborative pseudo-labeling. Starting with a lightweight classifier trained on limited labeled data, our method iteratively assigns pseudo-labels to unlabeled instances with the support of Multi-Agent Vision-Language Models (MA-VLMs). Un-labeled data on which the classifier and MA-VLMs agree are designated as the Agreed-Unknown set, while conflicting samples form the Disagreed-Unknown set. To enhance label reliability, MA-VLMs simulate dual perspectives, moderator and user, capturing both regulatory and subjective viewpoints. The classifier is optimized using a novel Positive-Negative-Unlabeled (PNU) loss, which jointly exploits labeled, Agreed-Unknown, and Disagreed-Unknown data while mitigating pseudo-label noise. Experiments on benchmark datasets demonstrate that our framework substantially outperforms baselines under limited supervision and approaches the performance of large-scale models
Gene Incremental Learning for Single-Cell Transcriptomics
Venue: arXiv cs.LG 2025
Year: 2025

Classes, as fundamental elements of Computer Vision, have been extensively studied within incremental learning frameworks. In contrast, tokens, which play essential roles in many research fields, exhibit similar characteristics of growth, yet investigations into their incremental learning remain significantly scarce. This research gap primarily stems from the holistic nature of tokens in language, which imposes significant challenges on the design of incremental learning frameworks for them. To overcome this obstacle, in this work, we turn to a type of token, gene, for a large-scale biological dataset--single-cell transcriptomics--to formulate a pipeline for gene incremental learning and establish corresponding evaluations. We found that the forgetting problem also exists in gene incremental learning, thus we adapted existing class incremental learning methods to mitigate the forgetting of genes. Through extensive experiments, we demonstrated the soundness of our framework design and evaluations, as well as the effectiveness of our method adaptations. Finally, we provide a complete benchmark for gene incremental learning in single-cell transcriptomics.
Known Meets Unknown: Mitigating Overconfidence in Open Set Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Open Set Recognition (OSR) requires models not only to accurately classify known classes but also to effectively reject unknown samples. However, when unknown samples are semantically similar to known classes, inter-class overlap in the feature space often causes models to assign unjustifiably high confidence to them, leading to misclassification as known classes -- a phenomenon known as overconfidence. This overconfidence undermines OSR by blurring the decision boundary between known and unknown classes. To address this issue, we propose a framework that explicitly mitigates overconfidence caused by inter-class overlap. The framework consists of two components: a perturbation-based uncertainty estimation module, which applies controllable parameter perturbations to generate diverse predictions and quantify predictive uncertainty, and an unknown detection module with distinct learning-based classifiers, implemented as a two-stage procedure, which leverages the estimated uncertainty to improve discrimination between known and unknown classes, thereby enhancing OSR performance. Experimental results on three public datasets show that the proposed framework achieves superior performance over existing OSR methods.
Modeling Fairness in Recruitment AI via Information Flow
Venue: arXiv cs.CY 2025
Year: 2025

Avoiding bias and understanding the real-world consequences of AI-supported decision-making are critical to address fairness and assign accountability. Existing approaches often focus either on technical aspects, such as datasets and models, or on high-level socio-ethical considerations - rarely capturing how these elements interact in practice. In this paper, we apply an information flow-based modeling framework to a real-world recruitment process that integrates automated candidate matching with human decision-making. Through semi-structured stakeholder interviews and iterative modeling, we construct a multi-level representation of the recruitment pipeline, capturing how information is transformed, filtered, and interpreted across both algorithmic and human components. We identify where biases may emerge, how they can propagate through the system, and what downstream impacts they may have on candidates. This case study illustrates how information flow modeling can support structured analysis of fairness risks, providing transparency across complex socio-technical systems.
ScoresActivation: A New Activation Function for Model Agnostic Global Explainability by Design
Venue: arXiv cs.LG 2025
Year: 2025

Understanding the decision of large deep learning models is a critical challenge for building transparent and trustworthy systems. Although the current post hoc explanation methods offer valuable insights into feature importance, they are inherently disconnected from the model training process, limiting their faithfulness and utility. In this work, we introduce a novel differentiable approach to global explainability by design, integrating feature importance estimation directly into model training. Central to our method is the ScoresActivation function, a feature-ranking mechanism embedded within the learning pipeline. This integration enables models to prioritize features according to their contribution to predictive performance in a differentiable and end-to-end trainable manner. Evaluations across benchmark datasets show that our approach yields globally faithful, stable feature rankings aligned with SHAP values and ground-truth feature importance, while maintaining high predictive performance. Moreover, feature scoring is 150 times faster than the classical SHAP method, requiring only 2 seconds during training compared to SHAP's 300 seconds for feature ranking in the same configuration. Our method also improves classification accuracy by 11.24% with 10 features (5 relevant) and 29.33% with 16 features (5 relevant, 11 irrelevant), demonstrating robustness to irrelevant inputs. This work bridges the gap between model accuracy and interpretability, offering a scalable framework for inherently explainable machine learning.
Causal computations in Semi Markovian Structural Causal Models using divide and conquer
Venue: arXiv cs.AI 2025
Year: 2025

Recently, Bjøru et al. proposed a novel divide-and-conquer algorithm for bounding counterfactual probabilities in structural causal models (SCMs). They assumed that the SCMs were learned from purely observational data, leading to an imprecise characterization of the marginal distributions of exogenous variables. Their method leveraged the canonical representation of structural equations to decompose a general SCM with high-cardinality exogenous variables into a set of sub-models with low-cardinality exogenous variables. These sub-models had precise marginals over the exogenous variables and therefore admitted efficient exact inference. The aggregated results were used to bound counterfactual probabilities in the original model. The approach was developed for Markovian models, where each exogenous variable affects only a single endogenous variable. In this paper, we investigate extending the methodology to \textit{semi-Markovian} SCMs, where exogenous variables may influence multiple endogenous variables. Such models are capable of representing confounding relationships that Markovian models cannot. We illustrate the challenges of this extension using a minimal example, which motivates a set of alternative solution strategies. These strategies are evaluated both theoretically and through a computational study.
H-CNN-ViT: A Hierarchical Gated Attention Multi-Branch Model for Bladder Cancer Recurrence Prediction
Venue: arXiv cs.CV 2025
Year: 2025

Bladder cancer is one of the most prevalent malignancies worldwide, with a recurrence rate of up to 78%, necessitating accurate post-operative monitoring for effective patient management. Multi-sequence contrast-enhanced MRI is commonly used for recurrence detection; however, interpreting these scans remains challenging, even for experienced radiologists, due to post-surgical alterations such as scarring, swelling, and tissue remodeling. AI-assisted diagnostic tools have shown promise in improving bladder cancer recurrence prediction, yet progress in this field is hindered by the lack of dedicated multi-sequence MRI datasets for recurrence assessment study. In this work, we first introduce a curated multi-sequence, multi-modal MRI dataset specifically designed for bladder cancer recurrence prediction, establishing a valuable benchmark for future research. We then propose H-CNN-ViT, a new Hierarchical Gated Attention Multi-Branch model that enables selective weighting of features from the global (ViT) and local (CNN) paths based on contextual demands, achieving a balanced and targeted feature fusion. Our multi-branch architecture processes each modality independently, ensuring that the unique properties of each imaging channel are optimally captured and integrated. Evaluated on our dataset, H-CNN-ViT achieves an AUC of 78.6%, surpassing state-of-the-art models. Our model is publicly available at https://github.com/XLIAaron/H-CNN-ViT.
Probabilistic Verification for Modular Network-on-Chip Systems (extended version)
Venue: arXiv cs.LO 2025
Year: 2025

Quantitative verification can provide deep insights into reliable Network-On-Chip (NoC) designs. It is critical to understanding and mitigating operational issues caused by power supply noise (PSN) early in the design process: fluctuations in network traffic in modern NoC designs cause dramatic variations in power delivery across the network, leading to unreliability and errors in data transfers. Further complicating these challenges, NoC designs vary widely in size, usage, and implementation. This case study paper presents a principled, systematic, and modular NoC modeling approach using the Modest language that closely reflects the standard hierarchical design approach in digital systems. Using the Modest Toolset, functional and quantitative correctness was established for several NoC models, all of which were instantiated from a generic modular router model. Specifically, this work verifies the functional correctness of a generic router, inter-router communication, and the entire NoC. Statistical model checking was used to verify PSN-related properties for NoCs of size up to 8x8.
Mind the Gap: Evaluating LLM Understanding of Human-Taught Road Safety Principles
Venue: arXiv cs.CV 2025
Year: 2025

Following road safety norms is non-negotiable not only for humans but also for the AI systems that govern autonomous vehicles. In this work, we evaluate how well multi-modal large language models (LLMs) understand road safety concepts, specifically through schematic and illustrative representations. We curate a pilot dataset of images depicting traffic signs and road-safety norms sourced from school text books and use it to evaluate models capabilities in a zero-shot setting. Our preliminary results show that these models struggle with safety reasoning and reveal gaps between human learning and model interpretation. We further provide an analysis of these performance gaps for future research.
The Battle of Metasurfaces: Understanding Security in Smart Radio Environments
Venue: arXiv cs.CR 2025
Year: 2025

Metasurfaces, or Reconfigurable Intelligent Surfaces (RISs), have emerged as a transformative technology for next-generation wireless systems, enabling digitally controlled manipulation of electromagnetic wave propagation. By turning the traditionally passive radio environment into a smart, programmable medium, metasurfaces promise advances in communication and sensing. However, metasurfaces also present a new security frontier: both attackers and defenders can exploit them to alter wireless propagation for their own advantage. While prior security research has primarily explored unilateral metasurface applications - empowering either attackers or defenders - this work investigates symmetric scenarios, where both sides possess comparable metasurface capabilities. Using both theoretical modeling and real-world experiments, we analyze how competing metasurfaces interact for diverse objectives, including signal power and sensing perception. Thereby, we present the first systematic study of context-agnostic metasurface-to-metasurface interactions and their implications for wireless security. Our results reveal that the outcome of metasurface "battles" depends on an interplay of timing, placement, algorithmic strategy, and hardware scale. Across multiple case studies in Wi-Fi environments, including wireless jamming, channel obfuscation for sensing and communication, and sensing spoofing, we demonstrate that opposing metasurfaces can substantially or fully negate each other's effects. By undermining previously proposed security and privacy schemes, our findings open new opportunities for designing resilient and high-assurance physical-layer systems in smart radio environments.
CORGI: Efficient Pattern Matching With Quadratic Guarantees
Venue: arXiv cs.AI 2025
Year: 2025

Rule-based systems must solve complex matching problems within tight time constraints to be effective in real-time applications, such as planning and reactive control for AI agents, as well as low-latency relational database querying. Pattern-matching systems can encounter issues where exponential time and space are required to find matches for rules with many underconstrained variables, or which produce combinatorial intermediate partial matches (but are otherwise well-constrained). When online AI systems automatically generate rules from example-driven induction or code synthesis, they can easily produce worst-case matching patterns that slow or halt program execution by exceeding available memory. In our own work with cognitive systems that learn from example, we've found that aggressive forms of anti-unification-based generalization can easily produce these circumstances. To make these systems practical without hand-engineering constraints or succumbing to unpredictable failure modes, we introduce a new matching algorithm called CORGI (Collection-Oriented Relational Graph Iteration). Unlike RETE-based approaches, CORGI offers quadratic time and space guarantees for finding single satisficing matches, and the ability to iteratively stream subsequent matches without committing entire conflict sets to memory. CORGI differs from RETE in that it does not have a traditional $β$-memory for collecting partial matches. Instead, CORGI takes a two-step approach: a graph of grounded relations is built/maintained in a forward pass, and an iterator generates matches as needed by working backward through the graph. This approach eliminates the high-latency delays and memory overflows that can result from populating full conflict sets. In a performance evaluation, we demonstrate that CORGI significantly outperforms RETE implementations from SOAR and OPS5 on a simple combinatorial matching task.